Overview

Dataset statistics

Number of variables22
Number of observations828782
Missing cells2599782
Missing cells (%)14.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory192.9 MiB
Average record size in memory244.0 B

Variable types

NUM15
CAT4
UNSUPPORTED3

Warnings

operation_car has constant value "828782" Constant
operation_date has a high cardinality: 21883 distinct values High cardinality
operation_st_esr is highly correlated with index_trainHigh correlation
index_train is highly correlated with operation_st_esrHigh correlation
adm has 828782 (100.0%) missing values Missing
danger has 828782 (100.0%) missing values Missing
gruz has 8386 (1.0%) missing values Missing
rod_train has 46070 (5.6%) missing values Missing
tare_weight has 828782 (100.0%) missing values Missing
weight_brutto has 45731 (5.5%) missing values Missing
ssp_station_id is highly skewed (γ1 = 123.3751596) Skewed
df_index has unique values Unique
adm is an unsupported type, check if it needs cleaning or further analysis Unsupported
danger is an unsupported type, check if it needs cleaning or further analysis Unsupported
tare_weight is an unsupported type, check if it needs cleaning or further analysis Unsupported
receiver has 103858 (12.5%) zeros Zeros
sender has 95858 (11.6%) zeros Zeros

Reproduction

Analysis started2021-04-14 19:41:58.391910
Analysis finished2021-04-14 19:45:25.626611
Duration3 minutes and 27.23 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct828782
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2069137.017
Minimum6
Maximum4189911
Zeros0
Zeros (%)0.0%
Memory size6.3 MiB
2021-04-14T22:45:26.203315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile204218.3
Q11040135.5
median2029278
Q33075855.5
95-th percentile4021362.85
Maximum4189911
Range4189905
Interquartile range (IQR)2035720

Descriptive statistics

Standard deviation1206481.986
Coefficient of variation (CV)0.5830846271
Kurtosis-1.160196735
Mean2069137.017
Median Absolute Deviation (MAD)1013957.5
Skewness0.06034024923
Sum1.714863515e+12
Variance1.455598783e+12
MonotocityStrictly increasing
2021-04-14T22:45:26.389757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
40981< 0.1%
 
28456351< 0.1%
 
5764391< 0.1%
 
5702961< 0.1%
 
5723451< 0.1%
 
5682511< 0.1%
 
26592601< 0.1%
 
26613091< 0.1%
 
26551661< 0.1%
 
28476801< 0.1%
 
28497291< 0.1%
 
28435861< 0.1%
 
28394921< 0.1%
 
6686481< 0.1%
 
28374471< 0.1%
 
28231161< 0.1%
 
7239191< 0.1%
 
28825131< 0.1%
 
28743251< 0.1%
 
28620431< 0.1%
 
28518061< 0.1%
 
27821761< 0.1%
 
6870731< 0.1%
 
27780821< 0.1%
 
5743901< 0.1%
 
Other values (828757)828757> 99.9%
 
ValueCountFrequency (%) 
61< 0.1%
 
71< 0.1%
 
151< 0.1%
 
161< 0.1%
 
211< 0.1%
 
241< 0.1%
 
331< 0.1%
 
371< 0.1%
 
551< 0.1%
 
561< 0.1%
 
ValueCountFrequency (%) 
41899111< 0.1%
 
41899101< 0.1%
 
41899091< 0.1%
 
41899071< 0.1%
 
41899031< 0.1%
 
41899001< 0.1%
 
41898971< 0.1%
 
41898881< 0.1%
 
41898831< 0.1%
 
41898801< 0.1%
 

index_train
Real number (ℝ≥0)

HIGH CORRELATION

Distinct28686
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.122461062e+14
Minimum1.04001941e+11
Maximum9.98100944e+14
Zeros0
Zeros (%)0.0%
Memory size6.3 MiB
2021-04-14T22:45:26.596691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.04001941e+11
5-th percentile8.417039369e+14
Q18.647056599e+14
median9.11605421e+14
Q39.54704897e+14
95-th percentile9.85906854e+14
Maximum9.98100944e+14
Range9.979969421e+14
Interquartile range (IQR)8.999923709e+13

Descriptive statistics

Standard deviation5.16135871e+13
Coefficient of variation (CV)0.05657857759
Kurtosis21.05474671
Mean9.122461062e+14
Median Absolute Deviation (MAD)4.679640817e+13
Skewness-1.40690022
Sum7.560531524e+20
Variance2.663962373e+27
MonotocityNot monotonic
2021-04-14T22:45:26.807959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
8.62201245e+146700.1%
 
8.622019499e+145010.1%
 
8.401096399e+14355< 0.1%
 
8.622014681e+14345< 0.1%
 
9.859068149e+14336< 0.1%
 
9.8590689e+14329< 0.1%
 
9.859068329e+14316< 0.1%
 
9.245018559e+14288< 0.1%
 
8.713041092e+14284< 0.1%
 
8.315040978e+14280< 0.1%
 
8.719063418e+14272< 0.1%
 
8.614069349e+14272< 0.1%
 
8.628030039e+14272< 0.1%
 
8.649023809e+14268< 0.1%
 
8.63806341e+14260< 0.1%
 
9.54704897e+14260< 0.1%
 
8.72504582e+14257< 0.1%
 
8.746030929e+14252< 0.1%
 
9.85906844e+14249< 0.1%
 
9.85906895e+14248< 0.1%
 
9.369030572e+14248< 0.1%
 
9.845024369e+14246< 0.1%
 
9.85906008e+14245< 0.1%
 
8.336034238e+14237< 0.1%
 
8.315040478e+14234< 0.1%
 
Other values (28661)82125899.1%
 
ValueCountFrequency (%) 
1.04001941e+111< 0.1%
 
1.090500295e+131< 0.1%
 
1.540004499e+131< 0.1%
 
1.540005295e+132< 0.1%
 
1.540005395e+132< 0.1%
 
3.300431497e+131< 0.1%
 
3.300476593e+132< 0.1%
 
3.690500899e+1334< 0.1%
 
6.000158698e+1331< 0.1%
 
6.000159598e+134< 0.1%
 
ValueCountFrequency (%) 
9.98100944e+142< 0.1%
 
9.98100943e+1410< 0.1%
 
9.98100942e+147< 0.1%
 
9.9810094e+147< 0.1%
 
9.98100939e+1410< 0.1%
 
9.98100938e+143< 0.1%
 
9.98100022e+143< 0.1%
 
9.9810002e+148< 0.1%
 
9.98100018e+149< 0.1%
 
9.98100017e+143< 0.1%
 

length
Real number (ℝ≥0)

Distinct73
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.03947307
Minimum0.7
Maximum2.6
Zeros0
Zeros (%)0.0%
Memory size6.3 MiB
2021-04-14T22:45:27.006644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile0.87
Q11
median1
Q31
95-th percentile1.59
Maximum2.6
Range1.9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1998735484
Coefficient of variation (CV)0.1922835273
Kurtosis8.976522469
Mean1.03947307
Median Absolute Deviation (MAD)0
Skewness2.986348406
Sum861496.57
Variance0.03994943536
MonotocityNot monotonic
2021-04-14T22:45:27.183273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
152950363.9%
 
0.879287611.2%
 
1.06792079.6%
 
0.83178952.2%
 
1.82171182.1%
 
1.85123001.5%
 
1.41108141.3%
 
0.8596071.2%
 
1.0583271.0%
 
1.2275960.9%
 
1.3667550.8%
 
1.8347130.6%
 
0.7937900.5%
 
1.0136110.4%
 
0.8632330.4%
 
1.1128850.3%
 
1.622980.3%
 
1.2720000.2%
 
1.3218540.2%
 
1.0314090.2%
 
1.3512390.1%
 
1.898910.1%
 
1.778770.1%
 
1.678610.1%
 
1.316790.1%
 
Other values (48)64440.8%
 
ValueCountFrequency (%) 
0.73< 0.1%
 
0.7821< 0.1%
 
0.7937900.5%
 
0.8212< 0.1%
 
0.83178952.2%
 
0.8596071.2%
 
0.8632330.4%
 
0.879287611.2%
 
0.889< 0.1%
 
0.9388< 0.1%
 
ValueCountFrequency (%) 
2.61< 0.1%
 
2.1356< 0.1%
 
1.935< 0.1%
 
1.92320< 0.1%
 
1.898910.1%
 
1.85123001.5%
 
1.84127< 0.1%
 
1.8347130.6%
 
1.82171182.1%
 
1.811< 0.1%
 

car_number
Real number (ℝ≥0)

Distinct379108
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59707244.2
Minimum20023164
Maximum98099997
Zeros0
Zeros (%)0.0%
Memory size6.3 MiB
2021-04-14T22:45:27.561957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum20023164
5-th percentile34147554.3
Q153563920
median58697339
Q363141584
95-th percentile94277423
Maximum98099997
Range78076833
Interquartile range (IQR)9577664

Descriptive statistics

Standard deviation13646097.57
Coefficient of variation (CV)0.2285501158
Kurtosis1.900152491
Mean59707244.2
Median Absolute Deviation (MAD)4774189
Skewness0.8157092536
Sum4.948428926e+13
Variance1.86215979e+14
MonotocityNot monotonic
2021-04-14T22:45:27.746564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5586482152< 0.1%
 
5582292849< 0.1%
 
5592753749< 0.1%
 
5570113048< 0.1%
 
5582294447< 0.1%
 
5562642847< 0.1%
 
5586422744< 0.1%
 
3202040642< 0.1%
 
5592452640< 0.1%
 
4460666340< 0.1%
 
3202046338< 0.1%
 
3416593638< 0.1%
 
5586446638< 0.1%
 
5586440937< 0.1%
 
3202025737< 0.1%
 
5595468937< 0.1%
 
3416447537< 0.1%
 
5599793637< 0.1%
 
5595255037< 0.1%
 
3202031537< 0.1%
 
5586484737< 0.1%
 
5595448136< 0.1%
 
5595435836< 0.1%
 
5599795136< 0.1%
 
5583215836< 0.1%
 
Other values (379083)82777099.9%
 
ValueCountFrequency (%) 
200231641< 0.1%
 
203568791< 0.1%
 
210824821< 0.1%
 
210943701< 0.1%
 
211361636< 0.1%
 
211364296< 0.1%
 
211364451< 0.1%
 
211376092< 0.1%
 
211384744< 0.1%
 
211391754< 0.1%
 
ValueCountFrequency (%) 
980999972< 0.1%
 
980999892< 0.1%
 
980999712< 0.1%
 
980999632< 0.1%
 
980999552< 0.1%
 
980999482< 0.1%
 
980999302< 0.1%
 
980999222< 0.1%
 
980999142< 0.1%
 
980999062< 0.1%
 

destination_esr
Real number (ℝ≥0)

Distinct1894
Distinct (%)0.2%
Missing5420
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean812924.1917
Minimum10002
Maximum998100
Zeros0
Zeros (%)0.0%
Memory size6.3 MiB
2021-04-14T22:45:27.954860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10002
5-th percentile76404
Q1860206
median887603
Q3944702
95-th percentile986103
Maximum998100
Range988098
Interquartile range (IQR)84496

Descriptive statistics

Standard deviation250954.9079
Coefficient of variation (CV)0.3087064088
Kurtosis3.658859912
Mean812924.1917
Median Absolute Deviation (MAD)48501
Skewness-2.22903076
Sum6.693308883e+11
Variance6.297836578e+10
MonotocityNot monotonic
2021-04-14T22:45:28.153545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
986103286053.5%
 
967808271053.3%
 
76404229602.8%
 
947005217402.6%
 
862108209362.5%
 
864902174152.1%
 
861406152091.8%
 
863007137181.7%
 
864809135731.6%
 
937906130491.6%
 
864207123521.5%
 
932207116671.4%
 
862201102231.2%
 
91160591381.1%
 
98930989951.1%
 
88760385591.0%
 
88790482941.0%
 
86020682501.0%
 
88380973930.9%
 
89310673670.9%
 
98470072040.9%
 
98570269020.8%
 
1850267370.8%
 
98780165810.8%
 
88140862750.8%
 
Other values (1869)50311560.7%
 
ValueCountFrequency (%) 
100029< 0.1%
 
1030327< 0.1%
 
113061< 0.1%
 
118049< 0.1%
 
130001< 0.1%
 
14906192< 0.1%
 
1540012< 0.1%
 
15701403< 0.1%
 
158051< 0.1%
 
160095460.1%
 
ValueCountFrequency (%) 
998100108< 0.1%
 
99750238< 0.1%
 
9971081< 0.1%
 
996904178< 0.1%
 
9968001< 0.1%
 
99660324< 0.1%
 
99630240< 0.1%
 
99580819< 0.1%
 
995507148< 0.1%
 
99540372< 0.1%
 

adm
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing828782
Missing (%)100.0%
Memory size6.3 MiB

danger
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing828782
Missing (%)100.0%
Memory size6.3 MiB

gruz
Real number (ℝ≥0)

MISSING

Distinct900
Distinct (%)0.1%
Missing8386
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean192001.8636
Minimum3009
Maximum999993
Zeros0
Zeros (%)0.0%
Memory size6.3 MiB
2021-04-14T22:45:28.366684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3009
5-th percentile3009
Q1161043
median161128
Q3221066
95-th percentile421034
Maximum999993
Range996984
Interquartile range (IQR)60023

Descriptive statistics

Standard deviation108975.9307
Coefficient of variation (CV)0.5675774631
Kurtosis7.442694572
Mean192001.8636
Median Absolute Deviation (MAD)9902
Skewness1.884757103
Sum1.575175609e+11
Variance1.187575347e+10
MonotocityNot monotonic
2021-04-14T22:45:28.567607image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
16112815040018.1%
 
161096570356.9%
 
3009522496.3%
 
161043454385.5%
 
161113360374.3%
 
161185355614.3%
 
141092291213.5%
 
236038247993.0%
 
214039216592.6%
 
161170211422.6%
 
161016178382.2%
 
91118172292.1%
 
421034162412.0%
 
211056155241.9%
 
161132130381.6%
 
81188119461.4%
 
321067118591.4%
 
161147115851.4%
 
232431105321.3%
 
22106696401.2%
 
28104895201.1%
 
32102980891.0%
 
22113680381.0%
 
17103068720.8%
 
21404368510.8%
 
Other values (875)17215320.8%
 
(Missing)83861.0%
 
ValueCountFrequency (%) 
3009522496.3%
 
1100521490.3%
 
111321< 0.1%
 
120085< 0.1%
 
13000129< 0.1%
 
14003232< 0.1%
 
1500677< 0.1%
 
1600917< 0.1%
 
1703524< 0.1%
 
1801929< 0.1%
 
ValueCountFrequency (%) 
9999937750.1%
 
99990210< 0.1%
 
9893997< 0.1%
 
9748165< 0.1%
 
9537163< 0.1%
 
95323711< 0.1%
 
95279812< 0.1%
 
9508792< 0.1%
 
9407162< 0.1%
 
9211902< 0.1%
 

loaded
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
1
457635 
2
371147 
ValueCountFrequency (%) 
145763555.2%
 
237114744.8%
 
2021-04-14T22:45:28.762406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-14T22:45:28.857753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:28.978862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
.82878233.3%
 
082878233.3%
 
145763518.4%
 
237114714.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number165756466.7%
 
Other Punctuation82878233.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
082878250.0%
 
145763527.6%
 
237114722.4%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.828782100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common2486346100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
.82878233.3%
 
082878233.3%
 
145763518.4%
 
237114714.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2486346100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
.82878233.3%
 
082878233.3%
 
145763518.4%
 
237114714.9%
 

operation_car
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
4
828782 
ValueCountFrequency (%) 
4828782100.0%
 
2021-04-14T22:45:29.137138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-14T22:45:29.239153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:29.342462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
482878233.3%
 
.82878233.3%
 
082878233.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number165756466.7%
 
Other Punctuation82878233.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
482878250.0%
 
082878250.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.828782100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common2486346100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
482878233.3%
 
.82878233.3%
 
082878233.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2486346100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
482878233.3%
 
.82878233.3%
 
082878233.3%
 

operation_date
Categorical

HIGH CARDINALITY

Distinct21883
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
2020-07-16 17:30:00
 
299
2020-07-24 22:25:00
 
298
2020-07-28 20:30:00
 
288
2020-07-21 22:30:00
 
285
2020-07-21 18:06:00
 
280
Other values (21878)
827332 
ValueCountFrequency (%) 
2020-07-16 17:30:00299< 0.1%
 
2020-07-24 22:25:00298< 0.1%
 
2020-07-28 20:30:00288< 0.1%
 
2020-07-21 22:30:00285< 0.1%
 
2020-07-21 18:06:00280< 0.1%
 
2020-07-26 00:18:00275< 0.1%
 
2020-07-29 10:05:00272< 0.1%
 
2020-07-16 12:50:00270< 0.1%
 
2020-07-25 00:31:00268< 0.1%
 
2020-07-24 20:10:00266< 0.1%
 
2020-07-18 21:01:00264< 0.1%
 
2020-07-18 17:21:00263< 0.1%
 
2020-07-17 17:00:00262< 0.1%
 
2020-07-26 08:14:00258< 0.1%
 
2020-07-24 18:01:00258< 0.1%
 
2020-07-23 17:12:00257< 0.1%
 
2020-07-31 18:45:00254< 0.1%
 
2020-07-16 16:00:00254< 0.1%
 
2020-07-18 08:30:00253< 0.1%
 
2020-07-19 07:05:00253< 0.1%
 
2020-07-27 20:40:00251< 0.1%
 
2020-07-22 20:00:00251< 0.1%
 
2020-07-19 17:00:00249< 0.1%
 
2020-07-24 18:41:00249< 0.1%
 
2020-07-27 09:00:00249< 0.1%
 
Other values (21858)82215699.2%
 
2021-04-14T22:45:29.601544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2433 ?
Unique (%)0.3%
2021-04-14T22:45:29.785438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length19
Mean length19
Min length19

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories4 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0504824432.1%
 
2255169416.2%
 
-165756410.5%
 
:165756410.5%
 
710808656.9%
 
110644646.8%
 
8287825.3%
 
34073252.6%
 
53767562.4%
 
43195802.0%
 
82561741.6%
 
62554071.6%
 
92424391.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1160294873.7%
 
Dash Punctuation165756410.5%
 
Other Punctuation165756410.5%
 
Space Separator8287825.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0504824443.5%
 
2255169422.0%
 
710808659.3%
 
110644649.2%
 
34073253.5%
 
53767563.2%
 
43195802.8%
 
82561742.2%
 
62554072.2%
 
92424392.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1657564100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
828782100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
:1657564100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common15746858100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0504824432.1%
 
2255169416.2%
 
-165756410.5%
 
:165756410.5%
 
710808656.9%
 
110644646.8%
 
8287825.3%
 
34073252.6%
 
53767562.4%
 
43195802.0%
 
82561741.6%
 
62554071.6%
 
92424391.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII15746858100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0504824432.1%
 
2255169416.2%
 
-165756410.5%
 
:165756410.5%
 
710808656.9%
 
110644646.8%
 
8287825.3%
 
34073252.6%
 
53767562.4%
 
43195802.0%
 
82561741.6%
 
62554071.6%
 
92424391.5%
 

operation_st_esr
Real number (ℝ≥0)

HIGH CORRELATION

Distinct673
Distinct (%)0.1%
Missing55
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean912890.7759
Minimum830003
Maximum998100
Zeros0
Zeros (%)0.0%
Memory size6.3 MiB
2021-04-14T22:45:29.949712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum830003
5-th percentile842301
Q1864705
median911605
Q3955406
95-th percentile985906
Maximum998100
Range168097
Interquartile range (IQR)90701

Descriptive statistics

Standard deviation48772.53683
Coefficient of variation (CV)0.05342647567
Kurtosis-1.360478284
Mean912890.7759
Median Absolute Deviation (MAD)46796
Skewness0.1373984419
Sum7.565372341e+11
Variance2378760349
MonotocityNot monotonic
2021-04-14T22:45:30.141557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
985906421015.1%
 
946801308573.7%
 
967600269103.2%
 
863007227312.7%
 
864207141061.7%
 
862201136341.6%
 
887603126391.5%
 
893106123161.5%
 
887904122881.5%
 
932207122791.5%
 
862108122211.5%
 
984502121271.5%
 
937906116131.4%
 
985609115001.4%
 
970001113121.4%
 
989309112041.4%
 
980003108051.3%
 
831504100291.2%
 
86490292181.1%
 
93690391571.1%
 
88380987731.1%
 
86020677460.9%
 
98770872430.9%
 
88140868860.8%
 
89210367960.8%
 
Other values (648)48223658.2%
 
ValueCountFrequency (%) 
83000312700.2%
 
8301079820.1%
 
83020015490.2%
 
83030417730.2%
 
8307098450.1%
 
83120310920.1%
 
83140022850.3%
 
831504100291.2%
 
831608127< 0.1%
 
831805219< 0.1%
 
ValueCountFrequency (%) 
99810061< 0.1%
 
99750266< 0.1%
 
99710810< 0.1%
 
99690492< 0.1%
 
99660323< 0.1%
 
99630253< 0.1%
 
99580819< 0.1%
 
9956001< 0.1%
 
995507146< 0.1%
 
99540358< 0.1%
 

operation_st_id
Real number (ℝ≥0)

Distinct673
Distinct (%)0.1%
Missing55
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2000675118
Minimum2000035090
Maximum2002026609
Zeros0
Zeros (%)0.0%
Memory size6.3 MiB
2021-04-14T22:45:30.630898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2000035090
5-th percentile2000035530
Q12000037062
median2000038840
Q32001930760
95-th percentile2001933494
Maximum2002026609
Range1991519
Interquartile range (IQR)1893698

Descriptive statistics

Standard deviation895367.6921
Coefficient of variation (CV)0.0004475327772
Kurtosis-1.520544385
Mean2000675118
Median Absolute Deviation (MAD)2476
Skewness0.6922906096
Sum1.658013489e+15
Variance8.016833041e+11
MonotocityNot monotonic
2021-04-14T22:45:30.836146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2000038976421015.1%
 
2000037862308573.7%
 
2000038600269103.2%
 
2001933494227312.7%
 
2001930816141061.7%
 
2001930776136341.6%
 
2000035530126391.5%
 
2000035966123161.5%
 
2000035564122881.5%
 
2000037064122791.5%
 
2001930794122211.5%
 
2000038950121271.5%
 
2000037532116131.4%
 
2000038970115001.4%
 
2000038624113121.4%
 
2000039132112041.4%
 
2000038840108051.3%
 
2001930534100291.2%
 
200003990892181.1%
 
200003749891571.1%
 
200003525287731.1%
 
200193076077460.9%
 
200003901672430.9%
 
200003519468860.8%
 
200003589067960.8%
 
Other values (648)48223658.2%
 
ValueCountFrequency (%) 
200003509024< 0.1%
 
20000351105430.1%
 
2000035130409< 0.1%
 
2000035140284< 0.1%
 
20000351624860.1%
 
20000351763< 0.1%
 
20000351826860.1%
 
200003519468860.8%
 
200003521267< 0.1%
 
20000352161< 0.1%
 
ValueCountFrequency (%) 
200202660926640.3%
 
20020266071< 0.1%
 
20020256672< 0.1%
 
20020252756< 0.1%
 
200202386769< 0.1%
 
200202350518< 0.1%
 
200202350316< 0.1%
 
20019335386980.1%
 
200193353613510.2%
 
200193353017440.2%
 

operation_train
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
5
783896 
71
 
43518
30
 
1366
35
 
2
ValueCountFrequency (%) 
578389694.6%
 
71435185.3%
 
3013660.2%
 
352< 0.1%
 
2021-04-14T22:45:31.052214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-14T22:45:31.173384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:31.329057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.054158995
Min length3

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
083014832.8%
 
.82878232.7%
 
578389831.0%
 
7435181.7%
 
1435181.7%
 
313680.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number170245067.3%
 
Other Punctuation82878232.7%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
083014848.8%
 
578389846.0%
 
7435182.6%
 
1435182.6%
 
313680.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.828782100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common2531232100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
083014832.8%
 
.82878232.7%
 
578389831.0%
 
7435181.7%
 
1435181.7%
 
313680.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2531232100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
083014832.8%
 
.82878232.7%
 
578389831.0%
 
7435181.7%
 
1435181.7%
 
313680.1%
 

receiver
Real number (ℝ≥0)

ZEROS

Distinct4331
Distinct (%)0.5%
Missing527
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean30857507.38
Minimum0
Maximum99964849
Zeros103858
Zeros (%)12.5%
Memory size6.3 MiB
2021-04-14T22:45:31.510519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11098923
median16176445
Q358786880
95-th percentile93149858
Maximum99964849
Range99964849
Interquartile range (IQR)57687957

Descriptive statistics

Standard deviation33069699.5
Coefficient of variation (CV)1.071690564
Kurtosis-1.047264971
Mean30857507.38
Median Absolute Deviation (MAD)16176445
Skewness0.6796297129
Sum2.555788477e+13
Variance1.093605025e+15
MonotocityNot monotonic
2021-04-14T22:45:31.704545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
010385812.5%
 
26635687240742.9%
 
71479207229272.8%
 
5757676212492.6%
 
1126631212442.6%
 
48134187196652.4%
 
93149858165512.0%
 
80298858162682.0%
 
20439962150721.8%
 
39513543150141.8%
 
10891709134781.6%
 
12776421128961.6%
 
2664409695761.2%
 
16187891961.1%
 
578516490031.1%
 
1499935588711.1%
 
112602281801.0%
 
18672079121.0%
 
8121359772050.9%
 
8326269290.8%
 
112539967570.8%
 
112664865830.8%
 
1478809065630.8%
 
7311603564790.8%
 
7960128656060.7%
 
Other values (4306)42709951.5%
 
ValueCountFrequency (%) 
010385812.5%
 
18595118< 0.1%
 
444749340.1%
 
5962541< 0.1%
 
6453763< 0.1%
 
8326269290.8%
 
10309417080.2%
 
1051476< 0.1%
 
1051829760.1%
 
10519922020.3%
 
ValueCountFrequency (%) 
999648492< 0.1%
 
9986372331< 0.1%
 
99849255117< 0.1%
 
998030521< 0.1%
 
997695851< 0.1%
 
9943515711< 0.1%
 
994175152< 0.1%
 
994154915550.1%
 
993390282< 0.1%
 
993328429< 0.1%
 

rodvag
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.5564491
Minimum20
Maximum99
Zeros0
Zeros (%)0.0%
Memory size6.3 MiB
2021-04-14T22:45:31.875077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q160
median60
Q370
95-th percentile96
Maximum99
Range79
Interquartile range (IQR)10

Descriptive statistics

Standard deviation15.67728566
Coefficient of variation (CV)0.2428461583
Kurtosis1.170603518
Mean64.5564491
Median Absolute Deviation (MAD)0
Skewness0.1729368058
Sum53503223
Variance245.7772857
MonotocityNot monotonic
2021-04-14T22:45:32.009871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
6052946663.9%
 
708822810.6%
 
90671828.1%
 
96606257.3%
 
40432465.2%
 
20240642.9%
 
9355720.7%
 
9550130.6%
 
9237340.5%
 
8716420.2%
 
9910< 0.1%
 
ValueCountFrequency (%) 
20240642.9%
 
40432465.2%
 
6052946663.9%
 
708822810.6%
 
8716420.2%
 
90671828.1%
 
9237340.5%
 
9355720.7%
 
9550130.6%
 
96606257.3%
 
ValueCountFrequency (%) 
9910< 0.1%
 
96606257.3%
 
9550130.6%
 
9355720.7%
 
9237340.5%
 
90671828.1%
 
8716420.2%
 
708822810.6%
 
6052946663.9%
 
40432465.2%
 

rod_train
Real number (ℝ≥0)

MISSING

Distinct25
Distinct (%)< 0.1%
Missing46070
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean34.05552106
Minimum3
Maximum89
Zeros0
Zeros (%)0.0%
Memory size6.3 MiB
2021-04-14T22:45:32.168705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10
Q110
median30
Q352
95-th percentile83
Maximum89
Range86
Interquartile range (IQR)42

Descriptive statistics

Standard deviation24.79548588
Coefficient of variation (CV)0.7280900456
Kurtosis-1.102973155
Mean34.05552106
Median Absolute Deviation (MAD)20
Skewness0.4553154027
Sum26655665
Variance614.8161202
MonotocityNot monotonic
2021-04-14T22:45:32.310554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
1034520541.7%
 
5211463613.8%
 
50619977.5%
 
83443665.4%
 
40390074.7%
 
20342944.1%
 
63333224.0%
 
58282723.4%
 
30273083.3%
 
72169462.0%
 
55161251.9%
 
89101041.2%
 
5479931.0%
 
816880.1%
 
574670.1%
 
824350.1%
 
56372< 0.1%
 
88329< 0.1%
 
64296< 0.1%
 
53252< 0.1%
 
65196< 0.1%
 
8747< 0.1%
 
340< 0.1%
 
6114< 0.1%
 
841< 0.1%
 
(Missing)460705.6%
 
ValueCountFrequency (%) 
340< 0.1%
 
1034520541.7%
 
20342944.1%
 
30273083.3%
 
40390074.7%
 
50619977.5%
 
5211463613.8%
 
53252< 0.1%
 
5479931.0%
 
55161251.9%
 
ValueCountFrequency (%) 
89101041.2%
 
88329< 0.1%
 
8747< 0.1%
 
841< 0.1%
 
83443665.4%
 
824350.1%
 
816880.1%
 
72169462.0%
 
65196< 0.1%
 
64296< 0.1%
 

sender
Real number (ℝ≥0)

ZEROS

Distinct2068
Distinct (%)0.2%
Missing527
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean41657844.58
Minimum0
Maximum99976605
Zeros95858
Zeros (%)11.6%
Memory size6.3 MiB
2021-04-14T22:45:32.488172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15757676
median48134187
Q369546824
95-th percentile93315475
Maximum99976605
Range99976605
Interquartile range (IQR)63789148

Descriptive statistics

Standard deviation33246640.51
Coefficient of variation (CV)0.7980883514
Kurtosis-1.431521086
Mean41657844.58
Median Absolute Deviation (MAD)32934737
Skewness0.1044068635
Sum3.450331806e+13
Variance1.105339105e+15
MonotocityNot monotonic
2021-04-14T22:45:32.685376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
09585811.6%
 
52682351457705.5%
 
56738657295853.6%
 
93315475287203.5%
 
68398528285713.4%
 
48134187198912.4%
 
69546824196342.4%
 
14788090191772.3%
 
80298858182892.2%
 
15199450163422.0%
 
26635687152291.8%
 
93149858147841.8%
 
5757676144831.7%
 
83262131961.6%
 
81213597119271.4%
 
28384864111531.3%
 
12776421111041.3%
 
55472826107161.3%
 
73472132105811.3%
 
18672086931.0%
 
1499935585401.0%
 
9442138685011.0%
 
7846542183821.0%
 
8119510378120.9%
 
2664409674090.9%
 
Other values (2043)34390841.5%
 
ValueCountFrequency (%) 
09585811.6%
 
444748130.1%
 
6453738< 0.1%
 
83262131961.6%
 
1052071< 0.1%
 
1052361< 0.1%
 
1054579< 0.1%
 
10870814< 0.1%
 
1097834< 0.1%
 
11897710750.1%
 
ValueCountFrequency (%) 
999766053< 0.1%
 
9986372345< 0.1%
 
9984925512< 0.1%
 
998350171< 0.1%
 
997695856< 0.1%
 
9943515732< 0.1%
 
994175151< 0.1%
 
9941549113830.2%
 
9902996062< 0.1%
 
989512823< 0.1%
 

ssp_station_esr
Real number (ℝ≥0)

Distinct701
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean902122.4535
Minimum104
Maximum998100
Zeros0
Zeros (%)0.0%
Memory size6.3 MiB
2021-04-14T22:45:32.897586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum104
5-th percentile840005
Q1864207
median920002
Q3967600
95-th percentile985906
Maximum998100
Range997996
Interquartile range (IQR)103393

Descriptive statistics

Standard deviation107582.0709
Coefficient of variation (CV)0.1192543989
Kurtosis37.78733846
Mean902122.4535
Median Absolute Deviation (MAD)49999
Skewness-5.433729669
Sum7.476628513e+11
Variance1.157390199e+10
MonotocityNot monotonic
2021-04-14T22:45:33.120336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
985906422735.1%
 
946801322933.9%
 
967600276533.3%
 
850007275373.3%
 
863007236782.9%
 
970001179212.2%
 
864207173292.1%
 
860206147181.8%
 
862201140351.7%
 
887904137861.7%
 
930004134471.6%
 
893106131801.6%
 
887603131771.6%
 
862108127941.5%
 
932207125571.5%
 
984502122421.5%
 
937906116521.4%
 
985609115001.4%
 
840005114911.4%
 
862305114271.4%
 
989205112621.4%
 
980003111471.3%
 
831504101921.2%
 
864902101151.2%
 
92570194631.1%
 
Other values (676)42191350.9%
 
ValueCountFrequency (%) 
1043< 0.1%
 
70668< 0.1%
 
1003108< 0.1%
 
1107107< 0.1%
 
1200142< 0.1%
 
140826< 0.1%
 
150174< 0.1%
 
1030336< 0.1%
 
185025360.1%
 
20706340< 0.1%
 
ValueCountFrequency (%) 
9981001< 0.1%
 
99750236< 0.1%
 
99690410< 0.1%
 
9968002< 0.1%
 
99660323< 0.1%
 
9963024300.1%
 
99580811< 0.1%
 
995507121< 0.1%
 
995403151< 0.1%
 
99510251< 0.1%
 

ssp_station_id
Real number (ℝ≥0)

SKEWED

Distinct673
Distinct (%)0.1%
Missing6665
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean2013704797
Minimum2000035070
Maximum2.000800815e+11
Zeros0
Zeros (%)0.0%
Memory size6.3 MiB
2021-04-14T22:45:33.326769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2000035070
5-th percentile2000035552
Q12000037064
median2000038840
Q32001930698
95-th percentile2001933498
Maximum2.000800815e+11
Range1.980800464e+11
Interquartile range (IQR)1893634

Descriptive statistics

Standard deviation1605297057
Coefficient of variation (CV)0.7971858927
Kurtosis15219.47187
Mean2013704797
Median Absolute Deviation (MAD)2328
Skewness123.3751596
Sum1.655500947e+15
Variance2.57697864e+18
MonotocityNot monotonic
2021-04-14T22:45:33.522111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2000038976422735.1%
 
2000037862322933.9%
 
2000038600276533.3%
 
2001930698275373.3%
 
2001933494236782.9%
 
2000038624179212.2%
 
2001930816173292.1%
 
2001930760147181.8%
 
2001930776140351.7%
 
2000035564137861.7%
 
2000036956134471.6%
 
2000035966131801.6%
 
2000035530131771.6%
 
2001930794127941.5%
 
2000037064125571.5%
 
2000038950122421.5%
 
2000037532116521.4%
 
2000038970115001.4%
 
2001930622114911.4%
 
2001930778114271.4%
 
2000039126112621.4%
 
2000038840111471.3%
 
2001930534101921.2%
 
2000039908101151.2%
 
200003686894631.1%
 
Other values (648)41524850.1%
 
ValueCountFrequency (%) 
20000350702< 0.1%
 
20000350901< 0.1%
 
200003511052760.6%
 
20000351305940.1%
 
200003514011< 0.1%
 
2000035162189< 0.1%
 
20000351767< 0.1%
 
20000351824180.1%
 
200003519469280.8%
 
200003521277< 0.1%
 
ValueCountFrequency (%) 
2.000800815e+1151< 0.1%
 
2.000800023e+113< 0.1%
 
200203666124< 0.1%
 
2002034347100< 0.1%
 
20020310273< 0.1%
 
20020301611< 0.1%
 
200203015912< 0.1%
 
2002030157271< 0.1%
 
20020300611< 0.1%
 
200202988193< 0.1%
 

tare_weight
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing828782
Missing (%)100.0%
Memory size6.3 MiB

weight_brutto
Real number (ℝ≥0)

MISSING

Distinct5885
Distinct (%)0.8%
Missing45731
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean3416.781459
Minimum0
Maximum12434
Zeros2305
Zeros (%)0.3%
Memory size6.3 MiB
2021-04-14T22:45:33.730044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile744
Q11640
median2496
Q35985
95-th percentile6505
Maximum12434
Range12434
Interquartile range (IQR)4345

Descriptive statistics

Standard deviation2202.639677
Coefficient of variation (CV)0.6446533686
Kurtosis-1.290286982
Mean3416.781459
Median Absolute Deviation (MAD)1382
Skewness0.4179188377
Sum2675514138
Variance4851621.548
MonotocityNot monotonic
2021-04-14T22:45:33.923226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
624626630.3%
 
023050.3%
 
626122740.3%
 
627722320.3%
 
625622190.3%
 
626521930.3%
 
624121310.3%
 
624521050.3%
 
625920260.2%
 
626219960.2%
 
630118170.2%
 
627217920.2%
 
624217900.2%
 
624717810.2%
 
627317730.2%
 
627017580.2%
 
627517150.2%
 
170616830.2%
 
625416760.2%
 
626716590.2%
 
169816480.2%
 
626616050.2%
 
629815810.2%
 
628315810.2%
 
169715690.2%
 
Other values (5860)73547988.7%
 
(Missing)457315.5%
 
ValueCountFrequency (%) 
023050.3%
 
51< 0.1%
 
191< 0.1%
 
21276< 0.1%
 
22101< 0.1%
 
2336< 0.1%
 
24221< 0.1%
 
25112< 0.1%
 
2655< 0.1%
 
2749< 0.1%
 
ValueCountFrequency (%) 
124342< 0.1%
 
899935< 0.1%
 
899860< 0.1%
 
899766< 0.1%
 
899329< 0.1%
 
89887< 0.1%
 
8987146< 0.1%
 
898564< 0.1%
 
898122< 0.1%
 
897233< 0.1%
 

Interactions

2021-04-14T22:43:28.739425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:29.249904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:29.747670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:30.209994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:30.689843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:31.134032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:31.586198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:32.112992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:32.714912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:33.263158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:33.830521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:34.411549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:35.048385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:35.725416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:36.364859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:36.934259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:37.585348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:38.152180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-14T22:43:39.620116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:40.097068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:40.607742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:41.103767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:41.545187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:42.001027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-14T22:43:43.913023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:44.519691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:45.096919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:45.585300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:46.034323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:46.574053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:47.235298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:47.865877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:48.420132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:48.919183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:49.381063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:49.898154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:50.449898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:50.977817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-14T22:43:51.917951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:52.378881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:52.839668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:53.305653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:53.767972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:54.274833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:54.754366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:55.530365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:56.609125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-14T22:43:57.659340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:58.112044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:58.557105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:59.139230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:43:59.645504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:00.192377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:00.672115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:01.113021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:01.575172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:01.990128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:02.441924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:02.856842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:03.300478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:03.741442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:04.200510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:04.638727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:05.086095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:05.523927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:05.994878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:06.408890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-14T22:44:07.294093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:07.749777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:08.277956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:08.717094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:09.214675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:09.639304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:10.050011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:10.494365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:10.927080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:11.311692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:11.720009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:12.173869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:12.580051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:12.962098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:13.408543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:13.816256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:14.250046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:14.808929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:15.270271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:15.770218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:16.211599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:16.693373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:17.153208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:17.656638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:18.127812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:18.570255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:18.999070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:19.459153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:19.902463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:20.429214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:20.863739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:21.288337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:21.715405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:22.128053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:22.580937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:23.026915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:23.506075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:23.967034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:24.426968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:24.849920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:25.320658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:25.759181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:26.240691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:26.677020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:27.214433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:27.667056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:28.149755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:28.646846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:29.119077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:29.571996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:29.998994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-14T22:44:30.927949image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:31.375158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:31.756443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-14T22:44:32.568097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:32.962914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-14T22:44:33.800166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-14T22:44:46.780463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:47.281467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:47.776694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:48.234443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:48.698776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:49.144787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:49.589970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:50.028644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:50.492752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:50.964196image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:51.412998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:51.842794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:52.301219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:52.753557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:53.179063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:53.625416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:54.062069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:54.530403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:54.956664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:55.416634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:55.843325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:56.283716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:56.722076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:57.144720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:57.594914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:58.011790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:58.442832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:58.892002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:59.402687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:44:59.829176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:00.282281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:00.716125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:01.160199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:01.559591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:02.004587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:02.437765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:02.896647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:03.338821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:03.774757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:04.253735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:04.705799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:05.390567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:05.883319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:06.377119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:06.825204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:07.322381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:07.772548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:08.239105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:08.667676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:09.163002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:09.614567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:10.053886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:10.525713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:10.978436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:11.468423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:11.949275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:12.431682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:12.905154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:13.395806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:13.853288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:14.334750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:14.785713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:15.249941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:15.678545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:16.179249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-04-14T22:45:34.120615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-14T22:45:34.563015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-14T22:45:35.022283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-14T22:45:35.502105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-14T22:45:35.839831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-14T22:45:18.000623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:19.866888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:23.439021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:45:24.254104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

df_indexindex_trainlengthcar_numberdestination_esradmdangergruzloadedoperation_caroperation_dateoperation_st_esroperation_st_idoperation_trainreceiverrodvagrod_trainsenderssp_station_esrssp_station_idtare_weightweight_brutto
068.624092e+141.062826326460005.0NaNNaN161170.01.04.02020-07-16 08:28:00862409.02.001933e+095.00.060.010.014788090.0460005.02.002029e+09NaN6246.0
178.878008e+141.062826987986103.0NaNNaN161128.01.04.02020-07-15 19:59:00887800.02.000036e+095.01126631.060.054.026648885.0920002.02.000037e+09NaN6237.0
2159.676009e+141.062845375913206.0NaNNaN161128.02.04.02020-07-16 12:50:00967600.02.000039e+095.013141274.060.010.069546824.0967600.02.000039e+09NaN1940.0
3169.795044e+141.062845052911605.0NaNNaN161128.02.04.02020-07-16 08:03:00979504.02.002027e+095.0161878.060.052.068398528.0979608.02.002027e+09NaN1481.0
4218.630076e+141.06284431176404.0NaNNaN161113.01.04.02020-07-16 11:20:00863007.02.001933e+095.039513543.060.010.026635687.0863007.02.001933e+09NaN6307.0
5248.630071e+141.062846928986103.0NaNNaN161113.01.04.02020-07-16 05:51:00863007.02.001933e+095.01126631.060.010.054998897.0863007.02.001933e+09NaN6248.0
6338.878008e+141.062846134986103.0NaNNaN161128.01.04.02020-07-15 19:59:00887800.02.000036e+095.01126631.060.054.026648885.0920002.02.000037e+09NaN6237.0
7379.676008e+141.062845730887904.0NaNNaN161113.02.04.02020-07-16 15:40:00967600.02.000039e+095.081195103.060.010.052682351.0967600.02.000039e+09NaN1687.0
8559.859068e+141.062844071861406.0NaNNaN161170.02.04.02020-07-16 07:22:00985906.02.000039e+095.020439962.060.063.069546824.0985906.02.000039e+09NaN2044.0
9569.859068e+141.062844071861406.0NaNNaN161170.02.04.02020-07-16 07:24:00985906.02.000039e+095.020439962.060.063.069546824.0985906.02.000039e+09NaN2044.0

Last rows

df_indexindex_trainlengthcar_numberdestination_esradmdangergruzloadedoperation_caroperation_dateoperation_st_esroperation_st_idoperation_trainreceiverrodvagrod_trainsenderssp_station_esrssp_station_idtare_weightweight_brutto
82877241898808.621081e+141.062817036NaNNaNNaN161128.01.04.02020-07-16 00:18:00862108.02.001931e+095.097728197.060.058.093149858.0862108.02.001931e+09NaN7939.0
82877341898839.245018e+141.062816723862201.0NaNNaN421034.02.04.02020-07-16 15:35:00924501.02.000037e+095.093149858.060.052.068398528.0924501.02.000037e+09NaN1696.0
82877441898888.647058e+141.062816178986103.0NaNNaN161185.01.04.02020-07-16 06:04:00864705.02.001934e+095.01126631.060.055.055472826.0864705.02.001934e+09NaN6299.0
82877541898979.044047e+141.062813985967808.0NaNNaN161128.01.04.02020-07-16 13:39:00904404.02.000036e+095.01126163.060.010.00.0910000.02.000036e+09NaN5610.0
82877641899009.859068e+141.062814017862201.0NaNNaN161016.02.04.02020-07-16 05:00:00985906.02.000039e+095.093149858.060.010.068398528.0985906.02.000039e+09NaN1994.0
82877741899038.631001e+141.062813852592204.0NaNNaN161043.01.04.02020-07-16 01:05:00863100.02.001931e+095.05757665.060.010.043830663.0592204.02.002016e+09NaN6295.0
82877841899078.630074e+141.062813340967808.0NaNNaN161128.01.04.02020-07-16 06:06:00863007.02.001933e+095.01126163.060.010.093149858.0863007.02.001933e+09NaN6275.0
82877941899099.683023e+141.062827514862201.0NaNNaN161128.02.04.02020-07-16 12:10:00968302.02.000039e+095.093149858.060.072.068398528.0968209.02.000039e+09NaN243.0
82878041899108.600099e+141.06282754876404.0NaNNaN161185.01.04.02020-07-16 03:29:00860009.02.001933e+095.039513543.060.010.076900054.0860009.02.001933e+09NaN6256.0
82878141899118.600099e+141.06282754876404.0NaNNaN161185.01.04.02020-07-16 03:30:00860009.02.001933e+095.039513543.060.010.076900054.0860009.02.001933e+09NaN6256.0